Defect detection in nano-imprint stamps with deep learning and low resolution microscope

Muthumbi AK (2022)

Publication Language: English

Publication Type: Thesis

Publication year: 2022

Abstract

Nanoimprint lithography is a mechanical based patterning technique where mold with
patterns are imprinted into photoresist. Nanoimprint molds considered in this work have
patterns of size 2-3 μm in diameter. During printing, misalignments and impurities in
the mold cause defects in the photoresist. Such defects are undesired and need to be
identified. A microscope with high numerical aperture objective lens is often used to image
the samples, though this leads to smaller field of views compared to low numerical aperture
lenses. Several unique fields of view are therefore required to image the entire sample. This
process is time consuming and can be prone to errors even when a mechanized scanner
is used. Computational imaging techniques can aid in reducing the tradeoff between
resolution and field of view. producing images with better quality. Algorithms are then
used to look for defects in the enhanced images. Advent of deep learning in the past
decade has led to algorithms that are much faster and more accurate than humans and
other algorithms at image analysis tasks. This has led to inclusion of deep learning in

almost every field, including microscopy.

This thesis set to perform segmentation of nanoimprint pillar images collected with a
low NA microscope, combining ideas from computational imaging with deep learning. To
achieve this, images of the sample were collected and later annotated to create masks.
The images and corresponding masks were then prepared as a dataset ready for training
various unet networks. Results from training a network with just a single image were
compared to results from training a network using a weighted sum of images. Results
indicate its easier to identify normal pillars in the sample for all networks, and different
networks perform differently in identifying defects. Networks that use both on and off axis
illumination images can identify defects that are missed with using networks that use only
on-axis illumination.

Authors with CRIS profile

Alex Kariuki Muthumbi Lehrstuhl für Elektronische Bauelemente

How to cite

APA:

Muthumbi, A.K. (2022). Defect detection in nano-imprint stamps with deep learning and low resolution microscope (Master thesis).

MLA:

Muthumbi, Alex Kariuki. Defect detection in nano-imprint stamps with deep learning and low resolution microscope. Master thesis, 2022.

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